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import datasets |
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import os |
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from datasets import load_dataset |
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from .m2d2_split_names import M2D2_SPLIT_NAMES |
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CITATION = """ |
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@inproceedings{reid2022m2d2, |
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title={ {M2D2}: A Massively Multi-Domain Language Modeling Dataset }, |
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author={ Machel Reid and Victor Zhong and Suchin Gururangan and Luke Zettlemoyer }, |
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booktitle={ EMNLP }, |
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year={ 2022 } |
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} |
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""" |
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DESCRIPTION = """ |
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M2D2 dataset from 'M2D2: A Massively Multi-Domain Language Modeling Dataset' |
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""" |
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FEATURES = datasets.Features({"text": datasets.Value("string")}) |
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def _URLS(split): |
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return f"https://huggingface.co/datasets/machelreid/m2d2/resolve/main/data/{split}.tar.gz" |
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class M2D2Config(datasets.BuilderConfig): |
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def __init__(self, features, citation, **kwargs): |
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super().__init__(**kwargs) |
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self.features = features |
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self.citation = citation |
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class M2D2(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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M2D2Config(name=name, features=FEATURES, citation=CITATION) |
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for name in M2D2_SPLIT_NAMES |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=DESCRIPTION, citation=CITATION, features=FEATURES |
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) |
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def _split_generators(self, dl_manager): |
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urls = _URLS(self.config.name) |
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data_dir = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "train.txt"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "valid.txt"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "test.txt"), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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with open(filepath, encoding="utf-8") as f: |
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for key, row in enumerate(f): |
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data = row.strip() |
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yield key, data |
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